def test_likelihoods(self):
"Makes sure that the sparsified likelihood function procedure is reasonable."
import time
nug = random.normal()**2 * .3
fs = array([0])
A, pos, age_distribution = ages_and_data([300], fs,
self.correction_factor_array,
[self.age_lims[0]])
t1 = time.time()
lp_small = mbgw.correction_factors.known_age_corr_likelihoods_f(
pos, A, self.correction_factor_array, linspace(-5, 5, 500), nug,
's')[0]
# print 'Small', time.time() - t1
t2 = time.time()
lp_large = mbgw.correction_factors.known_age_corr_likelihoods_f(
pos, A, self.correction_factor_array, linspace(-5, 5, 500), nug,
'l')[0]
# print 'Large', time.time() - t2
x = linspace(-5, 5, 100)
small = lp_small(x)
large = lp_large(x)
assert_almost_equal(small, large, 8)
def test_ages_and_data(self):
"Makes sure that the ages drawn by ages_and_data match the age distributions."
N_exam = ones(self.N) * 10000
A, pos, age_distribution = ages_and_data(N_exam, self.f_samp, self.correction_factor_array, self.age_lims)
empirical_age_distributions = []
for j in xrange(self.N):
A_ind = A[j]-self.age_lims[j][0]
empirical_age_distributions.append(array([sum(A_ind==i) for i in xrange(len(age_distribution[j]))]) / 10000.)
bin_sds = sqrt(age_distribution[j] * (1-age_distribution[j]) / 10000.)
assert(all(abs(age_distribution[j]-empirical_age_distributions[j]) < 4*bin_sds))
def test_ages_and_data(self):
"Makes sure that the ages drawn by ages_and_data match the age distributions."
N_exam = ones(self.N) * 10000
A, pos, age_distribution = ages_and_data(N_exam, self.f_samp,
self.correction_factor_array,
self.age_lims)
empirical_age_distributions = []
for j in xrange(self.N):
A_ind = A[j] - self.age_lims[j][0]
empirical_age_distributions.append(
array([
sum(A_ind == i) for i in xrange(len(age_distribution[j]))
]) / 10000.)
bin_sds = sqrt(age_distribution[j] * (1 - age_distribution[j]) /
10000.)
assert (all(
abs(age_distribution[j] - empirical_age_distributions[j]) < 4 *
bin_sds))
def test_likelihoods(self):
"Makes sure that the sparsified likelihood function procedure is reasonable."
import time
nug = random.normal()**2 * .3
fs = array([0])
A, pos, age_distribution = ages_and_data([300], fs, self.correction_factor_array, [self.age_lims[0]])
t1 = time.time()
lp_small = mbgw.correction_factors.known_age_corr_likelihoods_f(pos, A, self.correction_factor_array, linspace(-5,5,500), nug, 's')[0]
# print 'Small', time.time() - t1
t2 = time.time()
lp_large = mbgw.correction_factors.known_age_corr_likelihoods_f(pos, A, self.correction_factor_array, linspace(-5,5,500), nug, 'l')[0]
# print 'Large', time.time() - t2
x = linspace(-5, 5, 100)
small = lp_small(x)
large = lp_large(x)
assert_almost_equal(small, large, 8)
